Exploring interrelationships between cognition, functioning and quality of life in schizophrenia spectrum disorders: a Bayesian analysis of networks.

Cognitive impairment is a prevalent feature in schizophrenia spectrum disorders (SSDs), significantly impacting functional ability and quality of life. Although network analysis has been used in recent research, prior studies have frequently overlooked integrating existing knowledge, hindering the understanding of these complex associations. Using a Bayesian analysis of networks, we propose an innovative method incorporating prior knowledge through the utilization of multiple informed prior distributions. We analyzed data from 1150 individuals diagnosed with SSDs. Seven nodes, including cognitive variables, functional capacity, and subjective quality of life (SQL) indicators, were examined. Within a Bayesian framework, we estimated a network of partial associations and constructed a network to quantify the evidence of edge presence and absence, employing multiple informed priors derived from previous network studies. Our analysis uncovered robust associations between cognition (specifically verbal memory and processing speed) and functioning, as well as between functioning and SQL, supported by substantial evidence. While the absence of relationship between cognition and SQL was uncertain with a uniform prior, evidence of absence was observed with the use of informed priors from previous studies. This study underscores the intricate interplay among cognition, functioning, and quality of life within SSDs. Specifically, our results reveal associations between verbal memory and processing speed with functioning, whereas no association was found between cognition and quality of life. Integrating prior knowledge through a Bayesian framework facilitates nuanced insights and may contribute to more reliable inferences.